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Genotype-environment interaction was analysed in a winter-wheat breeding network using biadditive factorial regression models. This family of models generalizes both factorial regression and biadditive (or AMMI) models; it fits especially well whenabundant external information is available on genotypes and/or environments. Experiments were conducted at 5 sites in France during 1991-92. The approach, focused on environmental characterization, was performed with two kinds of covariates: (1) deviations of yield components measured on 4 probe genotypes; and (2) usual indicators of yield-limiting factors. The first step was based on analysis of a crop diagnosis measured on 4 probe genotypes. Difference of grain number to a threshold number (DKN)and reduction of 1000-grain weight from a potential value (RTKW) were used to characterize grain-number formation and grain-filling periods, respectively. Grain yield was analysed according to a biadditive factorial regression model using 8 environmental covariates (DKN and RTKW measured on each of 4 probe genotypes). In the second step, the usual indicators of yield-limiting factors were too numerous for the analysis of grain yield. Thus a selection of a subset of environmental covariates wasperformed on the analysis of DKN and RTKW for the 4 probe genotypes. Biadditive factorial regression models involved environmental covariates related to each deviation and included environmental main effect, sum of water deficits, an indicator of nitrogen stress, sumof daily radiation, high temperature, pressure of powdery mildew and lodging. The correlations of each environmental covariate to the synthetic variates helped to discard those poorly involved in interaction. The grain yield of 12 genotypes was interpreted with the retained covariates using biadditive factorial regression. The models explained about 75% of the interaction sums of squares. In addition, the biadditive factorial regression biplot gave relevant information about theinteraction of the genotypes (interaction pattern and sensitivities to environmental covariates) with respect to the environmental covariates and proved to be interesting for such an approach.

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